With “Using Data to Identify Good and Bad Actors” as its theme, our second BizTalks session this season featured Assistant Professor LYU Guodong from the Department of Information Systems, Business Statistics, and Operations Management, and Associate Professor WANG Shiheng from the Department of Accounting.
From Targeting to Transfer: Design of Allocation Rules in Cash Transfer Programs
Cash Transfer Programs (CTPs) have spread in the last decade to help fight extreme poverty in different parts of the world. A key issue here is to ensure that the cash is distributed to the targeted beneficiaries in an appropriate manner to meet the goals of the programs. How do we design efficient and egalitarian allocation rules for these programs? Prof. Lyu and his co-authors demonstrate how targeting methods can be integrated into the cash allocation problem to synthesize the impact of targeting errors on the design of the allocation rules. In particular, when the targeting errors are “well calibrated”, a simple predictive allocation rule is already optimal. Finally, although we only focus on the problem of poverty reduction (efficiency), the optimality conditions ensure that these allocation rules provide a common ex-ante service guarantee for each beneficiary in the allocation outcome (egalitarian).
Read here about Prof. Lyu’s study.
The Usefulness of Credit Ratings for Accounting Fraud Prediction
Prof. Wang examines whether and when credit ratings are useful for accounting fraud prediction. She finds that negative rating actions by Standard & Poor’s (S&P), an issuer-paid Credit Rating Agency (CRA), have predictive ability for fraud incremental to fraud prediction models (e.g., F-score) and other market participants. In contrast, rating actions by Egan-Jones Rating Company (EJR), an investor-paid CRA relying on public information, have less predictive ability, which is subsumed by S&P and other market participants. The results are robust to include firms not covered by EJR, using only rating downgrades, controlling for firm characteristics, and using alternative benchmarks. In sum, the results suggest that issuer-paid CRAs’ information advantage helps predict accounting fraud.
Find out more about Prof. WANG’s study here.